Emerging methods of collecting traffic information have produced an explosion in the amount of data available for analyzing traffic conditions. Among these emerging data collection methods are, for example, the ubiquitous use of mobile devices having applications configured thereon, and devices that generate traffic-related information such as global positioning systems (GPS) and Bluetooth communication systems. Each of these is capable of providing relevant information about traffic conditions. Additionally, other known systems such as video, radar, and traffic sensors also generate large amounts of traffic data. Together, all of this data enables advanced tools for processing and analyzing traffic conditions.
This vast amount of data has created a need for an automated, easy-to-use mechanism to access traffic information and present it in usable form for personnel responsible for traffic network management. There is presently no known system and method of automating the manipulation of traffic data to transform raw information into comparative analytics that improve the decision-making process for traffic management systems in an easy-to-use, configurable visualization tool. The current paradigm of acquiring, processing, and using information that focuses on devices, people, and information, is moving to automated means focusing on data, systems, and decisions, and there is a need in the art for automated systems and methods of enabling this paradigm shift for efficient comparative analytics for management of arterial transportation networks.
The ever-increasing amounts of data, demand improvements in ways to make sense of the information being collected and to enable more sophisticated performance monitoring of arterial transportation networks. For example, cities need to improve systems to better understand overall network performance. There is therefore a need for a system and method of assessing yearly, monthly, seasonal, and even daily or hourly variations in traffic conditions using real-time and historical speed, volume, capacity, incidents, and level-of-service (LOS) data, and there is also a need for using such data to assess the health of the entire transportation network (such as freeways, highways, and arterial or feeder segments) for which an agency or entity is responsible.
There is a further need for traffic data analytics that go beyond traditional “intersection LOS” methods that focus on improving conditions in the most heavily-congested areas and realization of savings in commuter time and fuel costs, both of which increase economic activity for the area(s) in which such transportation networks operate. There is an additional need to develop congestion analytics that enable traffic performance metrics such as a “state of the city” report card, which helps cities to better compete for regional funding.